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Agent Gateway

A unified gateway for selecting the right LLM model and securely accessing the right tools and agents across your enterprise.

Why It Matters for Enterprises

  • Model compliance and governance - Customers have clearance to only use specific approved models
  • Fine-tuned model support - Customers have a need to use their fine-tuned models for agents
  • Centralized control - Manage all model access, policies, and telemetry from one place
  • Seamless integration - Connect to multiple providers without rewriting applications
  • ContextForge Federates MCP servers, A2A agents, and REST APIs into one governed endpoint

Supported LLM Providers

The AI Gateway provides unified access to multiple foundation model providers:

Enterprise Platforms: - IBM watsonx.ai - AWS Bedrock - Azure OpenAI

Leading AI Providers: - OpenAI - Anthropic - Google (Gemini)

Open Source & Specialized: - Mistral - Ollama

Key Features & Capabilities

Model Gateway

  • Unified API & orchestration layer for multiple foundation models
  • Seamless model switching, routing, and failover without rewriting applications
  • Select the model of choice based on use case requirements
  • Configure models with parameters, model policies, and custom settings

Enterprise Controls

  • Enforce approved models and tools across the organization
  • Central telemetry and observability for all AI traffic
  • Consistent governance across hybrid and multi-cloud deployments
  • Policy enforcement for security, compliance, and cost management

Advanced Capabilities

  • Unified credential storage - Secure management of API keys and tokens
  • Load balancing - Distribute requests across multiple model instances
  • Failover and retries - Automatic fallback to alternative models
  • Custom API settings - Fine-tune request parameters per model
  • Usage tracking - Monitor consumption, costs, and performance metrics

Legacy Modernization

  • Virtualize existing REST/gRPC services as MCP tools
  • No need to rewrite agents - Integrate legacy systems seamlessly
  • Gradual migration path from traditional APIs to modern AI workflows

ContextForge

  • Federation Single catalog/entry point across multiple MCP and REST services
  • REST-to-MCP Adapter: Virtualize REST APIs as MCP-compliant tools
  • gRPC Translation: Reflection-based discovery and translation to MCP
  • Multi-Transport HTTP, JSON-RPC, WebSocket, SSE, stdio, streamable-HTTP
  • Built-in Security Auth, rate limiting, retries, OAuth token support

How It Works

  1. Select the model - Choose the most suitable model for your agent or use case
  2. Configure policies - Set parameters, rate limits, and governance rules
  3. Route requests - AI Gateway handles routing, authentication, and failover
  4. Monitor and optimize - Track usage, performance, and costs through central telemetry

Use Cases

Model Compliance & Governance

  • Approved model enforcement - Ensure only certified models are used in production
  • Audit and compliance - Track which models are used for which purposes
  • Regional restrictions - Route to compliant models based on data residency requirements

Fine-Tuned Model Deployment

  • Custom model integration - Deploy and manage organization-specific fine-tuned models
  • A/B testing - Compare performance between base and fine-tuned models
  • Gradual rollout - Route percentage of traffic to new model versions

Multi-Provider Model Management

  • Cost optimization - Route to most cost-effective model for each task
  • Performance optimization - Select fastest or most accurate model per use case
  • Vendor diversification - Avoid lock-in by supporting multiple providers

Legacy System Integration

  • API modernization - Expose legacy systems as AI-accessible tools
  • Hybrid workflows - Combine traditional APIs with modern AI agents
  • Incremental transformation - Modernize systems without full rewrites

Github Repository

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